The principal mission of the Core is to help NIH researchers with analyses of their fMRI (brain activation mapping) data. Along the way, we also help non-NIH investigators, mostly in the USA but also some abroad. Several levels of help are provided, from short-term immediate aid to long-term development and planning. Consultations: The shortest term help comprises in-person consultations with investigators about issues that arise in their research. The issues involved are quite varied, since there are many steps in carrying out fMRI data analyses and there are many different types of experiments. Common problems include: - How to set up experimental design so that data can be analyzed effectively? - Interpretation and correction of MRI imaging artifacts (a common one: subject head motion during scanning). - How to set up time series analysis to extract brain activation effects of interest, and to suppress non-activation artifacts? - Why don't AFNI results agree with SPM/FSL/something else? - How to analyze data to reveal connections between brain regions during specific mental tasks, or at rest? - How to recognize bad data? - How to carry out reliable inter-patient (group) statistical analysis, especially when non-MRI data (e.g., genetic information, age, disease rating) needs to be incorporated? - How to get good registration between the functional results and the anatomical reference images, and between the brain images from different subjects? - What sequence of programs is best for analyzing a particular kind of data? - Reports of real or imagined bugs in the AFNI software, as well as feature requests (small, large, and extravagant). There are familiar themes in many of these consultations, but each meeting and each experiment raises unique questions and usually requires digging into the goals and details of the research project in order to ensure that nothing central is being overlooked. Complex statistical issues are often raised. Often, software needs to be developed or modified to help researchers answer their specific questions. Helping with the Methods sections of papers is often part of our duties, as well. Educational Efforts: The Core developed (and updates) a 40-hour hands-on course on how to design and analyze fMRI data that was taught twice at the NIH during FY 2016 to over 200 students. All material for this continually evolving course (software, sample data, scripts, and PDF slides) are freely available on our Web site (https://afni.nimh.nih.gov). The course material includes several sample datasets that are used to illustrate the entire process, starting with images output by MRI scanners and continuing through to the collective statistical analysis of groups of subjects. By invitation, we also taught versions of this course at 5 non-NIH sites (expenses for these trips were sponsored by the hosts): Okazaki (Japan), Tulsa (Oklahoma), Hangzhou (China), Lincoln (Nebraska), and San Diego (California). In addition, more than 1200 AFNI forum postings were made by Core members, mostly in answer to queries from users. Algorithm and Software Development: The longest term support consists of developing new methods and software for fMRI data analysis, both to solve current problems and in anticipation of new needs. All of our software is incorporated into the AFNI package, which is Unix/Linux/Macintosh-based open-source and is available for download by anyone in source code or binary formats. New programs are created, and old programs modified, in response to specific user requests and in response to the Core's vision of what will be needed in the future. The Core also assists NIH labs in setting up computer systems for use with AFNI, and maintains an active Web site with a forum for questions (and answers) about fMRI data analysis. Notable developments during FY 2016 include: - A significant controversy erupted over statistical accuracy in fMRI group studies, after a paper in PNAS was published calling into question over 40,000 papers in this field (that specific claim was later rescinded by the authors). The Core expended significant effort to assess the magnitude of the issues raised in this paper and then fix the problems that we identified. The main AFNI group analysis program was modified to be more stringent, and was demonstrated to control the rate of false detections at the desired 5% level. These changes to prevent false positive results come at a cost of more false negative results (lost detections of significant changes between groups). New ideas generated while working on this first set of algorithm changes are now being implemented in software. We believe that this new combination of techniques will maintain proper statistical control of false positives while reducing the rate of false negative results. - Development of new statistical methods for carrying out inter-subject analyses of fMRI data that comes from experiments with naturalistic stimuli, such as watching a movie. Neither discrete-task nor resting-state analyses are appropriate for such studies, but the methods currently in use do not properly allow for the complex structure of this type of data. The new methods have been implemented in software, validated for statistical power, and two papers have been accepted for publication (1). - Development of tools for visualizing and analyzing data from Deep Brain Stimulation (DBS) studies; a paper was published about this software (4). - Extended the 3D macaque brain atlas that is distributed with AFNI, including connectivity. - Carried out analyses of AFNI software for visualizing and quantifying stroke lesions. Public Health Impact: From Oct 2015 to Aug 2016, the principal AFNI publication has been cited in 389 papers (cf Scopus). Most of our work supports basic research into brain function, but some of our work is more closely tied to or applicable to specific diseases: - We collaborate with Dr Alex Martin (NIMH) to apply our resting state analysis methods to autism spectrum disorder. - We consult frequently with NIMH researchers (e.g., Drs Pine, Ernst, Grillon, Leibenluft) working in mood and anxiety disorders. - We consult with Dr Elliot Stein (NIDA) in his research applying fMRI methods to drug abuse and addiction, and to Dr Momenan (NIAAA) in his studies of alcoholism. - Our Gd-DTPA nonlinear analysis method is used in the NIH Clinical Center to analyze data from brain cancer patients. - Our precise registration tools (for aligning fMRI scans to anatomical reference scans) are important for individual subject applications of brain mapping, such as pre-surgical fMRI planning. - Our real-time fMRI software is being used for studies on brain mapping feedback in neurological disorders, is used daily for quality control at the NIH fMRI scanners, and is also used at a few extramural sites. - Our statistical methods are being applied to epilepsy patients undergoing surgical planning with electro-corticography. Publications: #1-18 are papers that include Core authors. The remaining publications are from NIMH authors, who cited the primary AFNI paper, as an indication of their use of the Core facility. Papers from NIH but not NIMH authors are not included here, nor are papers from extramural researchers (due to limitations in the bibliography system).